Xiaofei Wang

Stephen L. George

Herbert Pang

Cancer Clinical Trials: Current and Controversial Issues in Design and Analysis provides statisticians with an understanding of the critical challenges currently encountered in oncology trials. Well-known statisticians from academic institutions, regulatory and government agencies (such as the U.S. FDA and National Cancer Institute), and the pharmaceutical industry share their extensive experiences in cancer clinical trials and present examples taken from actual trials. The book covers topics that are often perplexing and sometimes controversial in cancer clinical trials. Most of the issues addressed are also important for clinical trials in other settings. After discussing general topics, the book focuses on aspects of early and late phase clinical trials. It also explores personalized medicine, including biomarker-based clinical trials, adaptive clinical trial designs, and dynamic treatment regimes.

Current and Controversial Issues in Design and Analysis

Cancer Clinical Trials

Xu Chen

Dusit Niyato

Victor C. M. Leung

Yiwen Han

Xueqiang Yan

As an important enabler for changing people’s lives, advances in artificial intelligence (AI)-based applications and services are on the rise, despite being hindered by efficiency and latency issues. By focusing on deep learning as the most representative technique of AI, this book provides a comprehensive overview of how AI services are being applied to the network edge near the data sources, and demonstrates how AI and edge computing can be mutually beneficial. To do so, it introduces and discusses: 1) edge intelligence and intelligent edge; and 2) their implementation methods and enabling technologies, namely AI training and inference in the customized edge computing framework. Gathering essential information previously scattered across the communication, networking, and AI areas, the book can help readers to understand the connections between key enabling technologies, e.g. a) AI applications in edge; b) AI inference in edge; c) AI training for edge; d) edge computing for AI; and e) using AI to optimize edge. After identifying these five aspects, which are essential for the fusion of edge computing and AI, it discusses current challenges and outlines future trends in achieving more pervasive and fine-grained intelligence with the aid of edge computing.

Convergence of Edge Computing and Artificial Intelligence

Edge AI

Jianzhong Li

Limin Sun

Huadong Ma

Li Cui

Keqiu Li

Zenghua Zhao

This book constitutes the refereed proceedings of the 11th China Conference on Wireless Sensor Networks, CWSN 2017, held in Tianjin, China, in October 2017. The 28 revised full papers were carefully reviewed and selected from 213 submissions. The papers are organized in topical sections on wireless sensor networks; energy efficiency and harvesting; data fusion; mobile computing and social services.

11th China Wireless Sensor Network Conference, CWSN 2017, Tianjin, China, October 12-14, 2017, Revised Selected Papers

Wireless Sensor Networks

Haijun Zhang

Xiping Hu

Qiang Liu

This book constitutes the proceedings of the Third International Conference on 6G for Future Wireless Networks, 6GN 2020, held in Tianjin, China, in August 2020. The conference was held virtually due to the COVID-19 pandemic. The 45 full papers were selected from 109 submissions and present the state of the art and practical applications of 6G technologies. The papers are arranged thematically on network scheduling and optimization; wireless system and platform; intelligent applications; network performance evaluation; cyber security and privacy; technologies for private 5G/6G.

Third EAI International Conference, 6GN 2020, Tianjin, China, August 15-16, 2020, Proceedings

6GN for Future Wireless Networks

Xiaofei Wang

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